Oftentimes we come across data that is redundant and brings no value to the business such data is dropped in the transformation phase to save the storage space of the system. Null values, if present in the data, should be removed other than that, there are outliers often present in the data, which affect the analysis negatively they should be dealt with in the transformation phase. In addition to reformatting the data, there are other reasons too for the need for transformation of the data. Sorting-data is organized in a manner that increases efficiency.Spotting outliers-outliers are spotted and normalized.Duplication Removal-redundant data is removed.Standardization-uniform formatting is applied throughout.Cleansing-inconsistent and missing data are catered for.For that, the raw data undergoes a few transformation sub-processes, such as: In the transformation phase, the extracted raw data is transformed and compiled into a format that is suitable for the target system. These sources are either structured or unstructured, which is why the format of the data isn’t uniform at this stage. CRM (Customer Relationship Management) Software. In this phase, the data is extracted from multiple sources using SQL queries, Python codes, DBMS (database management systems), or ETL tools. These business intelligence tools are then used by businesses to make data-driven decisions. It’s a three-step process that extracts data from multiple sources, transforms it, and then loads it into business intelligence tools. It ensures the integrity of the data that is to be used for reporting, analysis, and prediction with machine learning models. Methodology of ETLĮTL makes it possible to integrate data from different sources into one place so that it can be processed, analyzed, and then shared with the stakeholders of businesses. In this article, we will look into the methodology of ETL, its use cases, its benefits, and how this process has helped form the modern data landscape. Though in all these different infrastructures, one process remained the same, the ETL process. Data marts have been converted to data warehouses, and when that hasn’t been enough, data lakes have been created. As a result, the modern data stack has evolved. Global data creation has increased exponentially, so much so that, as per Forbes, at the current rate, humans are doubling data creation every two years. This useful information is what helps businesses make data-driven decisions and grow. It is a process that integrates data from different sources into a single repository so that it can be processed and then analyzed so that useful information can be inferred from it. With these changed requirements for ETL processing, the question arises - are the traditional ETL tools equipped to process big data? In this first article, of a two-article series, we explore the current role of the ETL tools in the data warehouse environment and the new requirements from ETL with the influx of big data.ETL stands for “extract, transform, load”. This change in data volume, type and incoming speed, calls for platforms which can inexpensively process this high volume data in situ, i.e., in the context in which it physically lives and transform it at real-time streaming rate to keep up with its incoming speed. Internet of Things (IoT) can be seen as one of the drivers of the evolving data size and speed of data, considering the IoT datasets which include sensor data, video feeds, mobile geolocation data, product usage data, social media data, and log files. However, the world of data is constantly evolving. The ETL jobs are primarily batch driven and relational, developed and executed with a mature ETL tool. A traditional ETL process can take anything from a couple of hours or a day to complete. The main goal of ETL was to extract data from multiple data sources, transform the data according to business rules and load it to the target database. Traditional ETL was a key component of the data warehousing environment.
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